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InvisibleTiger/README.md

Hi there 👋

I am Jeevan Adhya, a passionate and driven individual with a love for robotics, programming, math, science, and technology. As a member of the FRC team 5417, Eagle Robotics and Eagle Spec Ops, for the past three years, I have gained invaluable experience and knowledge in these fields, and have had the opportunity to work collaboratively with like-minded individuals to design, build, and program robots that compete at a high level.

Currently, I am a junior at Allen High School's class of 2025, where I am continue to pursue my interests in these areas. I have had the opportunity to take accelerated courses in science and mathematics, which have further strengthened my skills in programming and problem-solving.

In addition to my academic pursuits, I have also gained hands-on experience with various hardware and software platforms, including Arduino, Raspberry Pi, and ROS (Robot Operating System). These experiences have enabled me to gain a deeper understanding of how software and hardware can be integrated to create complex systems and solve real-world problems.

I am committed to continued growth and development in these areas and am always looking for new challenges and opportunities to learn. My GitHub profile showcases some of my personal projects and contributions to open-source projects. I believe that through collaboration and shared knowledge, we can push the boundaries of what is possible in robotics and technology.

  • ⚒️ I’m well versed with ...

  • 🌱 I’m currently learning ...

  • 💻 I develop for ...

  • 💡 Development Environments I use ...

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  1. NeuralThinkv2 NeuralThinkv2 Public

    Python 1 1

  2. frc5417/CustomSwerve frc5417/CustomSwerve Public archive

    Find us in action @ FIRST Robotics Competition, Crescendo. We're competing as Eagle Spec Ops, 5788.

    Java 6

  3. frc5417/Swerve-Documentation frc5417/Swerve-Documentation Public

    Documentation for our CustomSwerve algorithm.

    HTML

  4. Text Classification Model With Sci-K... Text Classification Model With Sci-Kit Learn and Pandas That Classifies Text as Positive or Negative.
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    import joblib
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    # Load the saved model from the file
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    loaded_model = joblib.load('trained_model.pkl')
    5